Trend-cycle Estimation Using Fuzzy Transform and Its Application for Identifying Bull and Bear Phases in Markets

Q1 Economics, Econometrics and Finance Intelligent Systems in Accounting, Finance and Management Pub Date : 2020-06-11 DOI:10.1002/isaf.1473
Linh Nguyen, Vilém Novák, Soheyla Mirshahi
{"title":"Trend-cycle Estimation Using Fuzzy Transform and Its Application for Identifying Bull and Bear Phases in Markets","authors":"Linh Nguyen,&nbsp;Vilém Novák,&nbsp;Soheyla Mirshahi","doi":"10.1002/isaf.1473","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This paper is focused on one of the fundamental problems in financial time-series analysis; namely, the identification of the historical bull and bear phases. We start with the proof that the trend-cycle can be well estimated using the technique of a higher degree fuzzy transform. Then, we suggest a mathematical definition of the bull and bear phases and provide a novel technique for their identification. As a consequence, the turning points (i.e. the points where the market changes its phase) are detected. We illustrate our methodology on several examples.</p>\n </div>","PeriodicalId":53473,"journal":{"name":"Intelligent Systems in Accounting, Finance and Management","volume":"27 3","pages":"111-124"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/isaf.1473","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Systems in Accounting, Finance and Management","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/isaf.1473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Economics, Econometrics and Finance","Score":null,"Total":0}
引用次数: 1

Abstract

This paper is focused on one of the fundamental problems in financial time-series analysis; namely, the identification of the historical bull and bear phases. We start with the proof that the trend-cycle can be well estimated using the technique of a higher degree fuzzy transform. Then, we suggest a mathematical definition of the bull and bear phases and provide a novel technique for their identification. As a consequence, the turning points (i.e. the points where the market changes its phase) are detected. We illustrate our methodology on several examples.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于模糊变换的趋势周期估计及其在市场牛熊阶段识别中的应用
本文主要研究金融时间序列分析中的一个基本问题;即对历史牛市和熊市阶段的识别。首先证明了利用高阶模糊变换技术可以很好地估计趋势周期。然后,我们提出了牛和熊阶段的数学定义,并提供了一种新的技术来识别它们。因此,转折点(即市场改变其阶段的点)被检测到。我们用几个例子来说明我们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Intelligent Systems in Accounting, Finance and Management
Intelligent Systems in Accounting, Finance and Management Economics, Econometrics and Finance-Finance
CiteScore
6.00
自引率
0.00%
发文量
0
期刊介绍: Intelligent Systems in Accounting, Finance and Management is a quarterly international journal which publishes original, high quality material dealing with all aspects of intelligent systems as they relate to the fields of accounting, economics, finance, marketing and management. In addition, the journal also is concerned with related emerging technologies, including big data, business intelligence, social media and other technologies. It encourages the development of novel technologies, and the embedding of new and existing technologies into applications of real, practical value. Therefore, implementation issues are of as much concern as development issues. The journal is designed to appeal to academics in the intelligent systems, emerging technologies and business fields, as well as to advanced practitioners who wish to improve the effectiveness, efficiency, or economy of their working practices. A special feature of the journal is the use of two groups of reviewers, those who specialize in intelligent systems work, and also those who specialize in applications areas. Reviewers are asked to address issues of originality and actual or potential impact on research, teaching, or practice in the accounting, finance, or management fields. Authors working on conceptual developments or on laboratory-based explorations of data sets therefore need to address the issue of potential impact at some level in submissions to the journal.
期刊最新文献
The Technological Innovation of the Metaverse in Financial Sector: Current State, Opportunities, and Open Challenges Issue Information Liquidity forecasting at corporate and subsidiary levels using machine learning Identification of fraudulent financial statements through a multi-label classification approach Predicting carbon and oil price returns using hybrid models based on machine and deep learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1